code: def test_tcn_separator_forward_backward_complex(
      input_dim,
      layer,
      num_spk,
      nonlinear,
      stack,
      bottleneck_dim,
      hidden_dim,
      kernel,
      causal,
      norm_type,
  ):
      model = TCNSeparator(
          input_dim=input_dim,
          num_spk=num_spk,
          layer=layer,
          stack=stack,
          bottleneck_dim=bottleneck_dim,
          hidden_dim=hidden_dim,
          kernel=kernel,
          causal=causal,
          norm_type=norm_type,
          nonlinear=nonlinear,
      )
      model.train()
  
      real = torch.rand(2, 10, input_dim)
      imag = torch.rand(2, 10, input_dim)
      x = ComplexTensor(real, imag)
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      masked, flens, others = model(x, ilens=x_lens)
  
      assert isinstance(masked[0], ComplexTensor)
      assert len(masked) == num_spk
  
      masked[0].abs().mean().backward()
  
file_path: espnet/test/espnet2/enh/separator/test_tcn_separator.py
imports: import pytest
  import torch
  from torch import Tensor
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.tcn_separator import TCNSeparator
label_desc: Use image node_id 1 for calling a global function with example usage: test_tcn_separator_forward_backward_complex(input_dim, layer, num_spk, nonlinear, stack, bottleneck_dim, hidden_dim, kernel, causal, norm_type) without return types